Test
Pengyuan Su (ps3195)
11/20/2020
Import data (Mainly focus on publisher and their console)
##
## -- Column specification --------------------------------------------------------
## cols(
## title = col_character(),
## console = col_character(),
## genre = col_character(),
## release_date = col_date(format = ""),
## publisher = col_character(),
## developer = col_character(),
## total_sale = col_double(),
## na_sale = col_double(),
## pal_sale = col_double(),
## japan_sale = col_double(),
## other_sale = col_double()
## )Plot for year and sale, Plot for year and console
year of game vs sale
## `summarise()` ungrouping output (override with `.groups` argument)test for the normality
##
## Shapiro-Wilk normality test
##
## data: sales
## W = 0.86719, p-value = 8.884e-05year vs console
Publisher (TOP 10)
## `summarise()` ungrouping output (override with `.groups` argument)## Adding missing grouping variables: `title`Select Period 2005-2015
## `summarise()` ungrouping output (override with `.groups` argument)## Adding missing grouping variables: `title`Focus on Nitendo
Nintendo Console
| console | time |
|---|---|
| Game & Watch | 1980-1991 |
| NES | 1983-1995 |
| Game Boy (GB) | 1989-2003 |
| SNES | 1990-1998 |
| Virtual Boy | 1995-1996 |
| N64 | 1996-2003 |
| GameCube (GC) | 2001-2007 |
| Game Boy Advance | 2001-2008 |
| DS | 2004-2014 |
| Wii | 2006-2013 |
| Nintendo 3DS (3DS) | 2011- |
| Will U (WiiU) | 2012-2016 |
| NES | 2016-2017 |
| Switch (NS) | 2017- |
games on each Nintendo Console during 2005-2015
| console | n |
|---|---|
| 3DS | 59 |
| DS | 128 |
| GBA | 22 |
| GC | 15 |
| Wii | 64 |
| WiiU | 27 |
## Analysis of Variance Table
##
## Response: nin
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(ind) 5 763.3 152.65 3.211 0.007667 **
## Residuals 309 14689.8 47.54
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Pairwise comparisons using t tests with pooled SD
##
## data: new_data$nin and new_data$ind
##
## 1 2 3 4 5
## 2 1.000 - - - -
## 3 0.204 0.139 - - -
## 4 1.000 1.000 0.048 - -
## 5 1.000 1.000 0.031 1.000 -
## 6 1.000 1.000 0.172 1.000 1.000
##
## P value adjustment method: bonferroni## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = nin ~ factor(ind), data = new_data)
##
## $`factor(ind)`
## diff lwr upr p adj
## 2-1 0.66686755 -3.9272530 5.2609880 0.9983927
## 3-1 3.92594329 -0.6114860 8.4633726 0.1328747
## 4-1 -1.13250842 -6.8114299 4.5464130 0.9927760
## 5-1 0.64547454 -3.5418828 4.8328319 0.9978559
## 6-1 -1.10629630 -7.4736383 5.2610457 0.9962123
## 3-2 3.25907574 -0.3095221 6.8276736 0.0957558
## 4-2 -1.79937596 -6.7386851 3.1399332 0.9023656
## 5-2 -0.02139301 -3.1327573 3.0899712 1.0000000
## 6-2 -1.77316384 -7.4906519 3.9443243 0.9488801
## 4-3 -5.05845170 -9.9450759 -0.1718275 0.0376380
## 5-3 -3.28046875 -6.3074985 -0.2534390 0.0249517
## 6-3 -5.03223958 -10.7042755 0.6397963 0.1147198
## 5-4 1.77798295 -2.7854361 6.3414020 0.8740112
## 6-4 0.02621212 -6.5945035 6.6469278 1.0000000
## 6-5 -1.75177083 -7.1478517 3.6443100 0.9382788